Existing works evaluate DVS models with image metrics on the full image, e.g., PSNR, SSIM [1]} and LPIPS [2]}, following novel-view synthesis evaluation on static scenes [3]}.
However, in dynamic scenes, particularly for monocular capture with multi-camera validation, the
test view contains regions that may not have been observed at all by the training camera.
To circumvent this issue without resorting to camera teleportation, for each pixel in the test image, we propose co-visibility masking, which tests how many times a test pixel has been observed in the training images.
Specifically, we use optical flow to compute correspondences between every test image and the training images, and only keep test pixels that have enough correspondences in the training images via thresholding.
This results in a mask, illustrated in Figure REF , which we use to confine the image metrics.
We follow the common practice from the image generation literature and adopt masked metrics, mPSNR and mLPIPS [4]}, [5]}.
Note that NSFF [6]} adopts similar metrics but for evaluating the rendering quality on foreground versus background regions.
We additionally report mSSIM by partial convolution [7]}, which only considers seen regions during its computation. More details are in the Appendix.
Using masked image metrics, we quantify the performance gap in rendering when a model is trained with or without multi-view cues in Section REF .
